This disclosure relates generally to video camera systems and more particularly to steerable video surveillance cameras.
Most currently available video surveillance analytics systems rely on background subtraction to detect objects of interest by comparing incoming frames of video with a background model that provides a reference representation of what a video camera should see if no moving objects were present. The background model is created by a background maintenance procedure that learns the normal appearance of each pixel or local area of the field of view of the camera. These types of video surveillance analytics systems work on the assumption that the camera does not move and that any pixel will continue to see the same region of the real world. If this assumption is violated, i.e., the camera is moved, then a pixel will receive light from a different part of the scene from the real world and differences not due to scene changes will be detected. The analytics component of the surveillance system detects these differences through background subtraction, and because the detected differences are not due to scene changes, the system generates many false positives, creating tracks for artifacts that are not due to moving objects. In addition, the system is quite likely to fail to track true moving objects because of the number of false tracks being generated.
Camera motions that can cause false positives to occur in video surveillance analytics systems that use background subtraction can occur for several reasons. For example, wind and vibration can cause the camera to make small movements that result in the camera oscillating around its normal position. Also, steerable surveillance cameras that are controlled by an operator such as a security guard or by an automated procedure that moves the camera can be subject to false positives because of the visual changes caused by the camera motion. Other reasons for camera motion can be the result of direct physical movement of the camera. For example, a maintenance worker could turn the camera, a truck could collide with the camera or an intruder could turn the camera so that it could no longer be used to observe activity in a certain area under surveillance.
Various approaches have been employed to stabilize camera motions that occur for the above-noted reasons. These stabilization approaches can use mechanical, electromechanical or electronic methods to remove the effects of movement from the images delivered to the background subtraction detection component of the video surveillance analytics systems. Mechanical and electromechanical methods may move the lens or sensor of the camera in such a way that the image formed on the sensor maintains the same alignment with it, while electronic methods may detect the offset in the obtained image and shift it back to counteract the detected motion.
These approaches work well in stabilizing small camera motions but are not effective in stabilizing large camera motions that are beyond the range of mechanical actuators' ability to move the lens or sensor to compensate or motions that are so great that compensation mechanisms introduce image distortions of other kinds that are in themselves problematic. In addition, these approaches fail to work well for camera motions that cannot be dealt with by a compensation method that can compensate for movements of the camera such as the twisting of the camera about its optical axis.